Murray-Sunset National Park

The Challenge

Malleefowl occupy semi-arid mallee scrub on the fringes of relatively fertile areas in southern Australia and are listed as threatened across their range.

Detection of malleefowl mounds has historically been done by people walking through the bush in a line a few metres apart. Covering large tracts of land by this method is time consuming, expensive and presents a range of work health and safety issues.

The Victorian Malleefowl Recovery Group and Latrobe University were seeking to have malleefowl surveys undertaken over a 250 km transect in Murray-Sunset National Park in north western Victoria. It was estimated that undertaking traditional ground survey of the 600 km2 area would require about 20,000 person hours to complete.

The Solution

The use of remotely sensed LiDAR and imagery data allows large tracts of land to be surveyed quickly and cost effectively. Once the LiDAR data points have been classified as ground, vegetation, water or manmade features, advanced feature detection algorithms can be applied to detect potential malleefowl mound sites across wide areas.

Anditi was engaged to acquire high-resolution LiDAR and imagery data, classify the LiDAR, and use its patented near-ground feature detection algorithms to identify potential sites across the 600 km2 survey area.

LiDAR

Imagery

Automatic point cloud classification

Advanced feature detection

Fully scalable analysis

Malleefowl mound in a LiDAR point cloud

The Outcome

Analysis of the captured and classified LiDAR data identified 676 high probability mound candidates within the survey area. 542 of the potential mound sites had not previously been recorded.

The potential malleefowl mounds were identified in less than 0.5% of the time it would have taken to perform a traditional ground survey.

The use of remote sensing provided a permanent 3D digital record of the location and size of the mounds and the characteristics of the surrounding vegetation and terrain. This information can be viewed, assessed against key environmental indicators such as known fire history, and discussed with agencies and researchers using a 3D viewer developed for the project.

High quality results

Important new habitat sites identified

Faster completion

Outcomes achieved in a fraction of the time

Enduring value

Permanent contextual record created

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